Parametric garment pattern models are widely used to generate 2D and 3D garment representations in reconstruction and simulation tasks. However, existing models are often difficult to adapt for specific purposes due to limited variations in base pattern shapes and the rigid, hardcoded handling of body and garment measurements. To address these challenges, we propose GarmentoPIA, a system that semi-automatically generates parametric garment pattern models from garment drafting literature using an LLM-based intelligent agent module. With GarmentoPIA, users can select garment drafting references and generate various pattern models that incorporate tailoring parameters specified in the source material. To improve adaptability across references, the system employs a prompt self-refinement mechanism that iteratively updates its instructions during model generation. This generation process is preceded by a manual preprocessing phase that first normalizes raw drafting instructions into a structured format. We furthermore introduce a garment domain-specific language (DSL), composed of explicit function calls corresponding to drafting components, to produce executable models that, once generated, run independently of any LLM—enhancing usability and accessibility. We validate the effectiveness of our approach through quantitative and qualitative evaluations.